The proceedings contain 194 papers. The topics discussed include: custom features of a large cluster batch scheduler;allocation management solutions for high performance computing;using marginal analysis for parallel ...
ISBN:
(纸本)1932415599
The proceedings contain 194 papers. The topics discussed include: custom features of a large cluster batch scheduler;allocation management solutions for high performance computing;using marginal analysis for parallel job scheduling with faulty processors;resource management in active networks;strategies for cache invalidation of location dependent data in mobile environment;an evaluation of singular value computation by the discrete Lotka-Volterra system;evolutionary algorithm based on schemata exploiter;a restricted sample distribution of simple deterministic languages and its learnability;an efficient algorithm for approximate solution of the vector cost assignment problem;scalable analysis of distributed workflow traces;constituting the need for flexibility in distributed operating systems;a framework for distributed human tracking;optimizing distributed query processing;and an efficient non-block synchronous checkpointing scheme for distributed systems.
The proceedings contain 194 papers. The topics discussed include: custom features of a large cluster batch scheduler;allocation management solutions for high performance computing;using marginal analysis for parallel ...
ISBN:
(纸本)9781932415582
The proceedings contain 194 papers. The topics discussed include: custom features of a large cluster batch scheduler;allocation management solutions for high performance computing;using marginal analysis for parallel job scheduling with faulty processors;resource management in active networks;strategies for cache invalidation of location dependent data in mobile environment;an evaluation of singular value computation by the discrete Lotka-Volterra system;evolutionary algorithm based on schemata exploiter;a restricted sample distribution of simple deterministic languages and its learnability;an efficient algorithm for approximate solution of the vector cost assignment problem;scalable analysis of distributed workflow traces;constituting the need for flexibility in distributed operating systems;a framework for distributed human tracking;optimizing distributed query processing;and an efficient non-block synchronous checkpointing scheme for distributed systems.
The proceedings contain 144 papers. The topics discussed include: iterative techniques for maximizing stochastic robustness of a static resource allocation in periodic sensor driven clusters;new heuristics for rotatio...
ISBN:
(纸本)1601320841
The proceedings contain 144 papers. The topics discussed include: iterative techniques for maximizing stochastic robustness of a static resource allocation in periodic sensor driven clusters;new heuristics for rotation scheduling;an optimizing distributed scheduler for high-level synthesis;adaptive software transactional memory: a dynamic approach to contention management;ECL experiments control language for scientific computing;implementation of a distributed environment by software architecture;an investigation into soft-state protocol parameters;a barrier synchronization protocol for broadcast networks based on dynamic access control;two-tier energy-aware routing protocol for wireless sensor networks;efficient forward error correction for reliable transmission in packet networks;a new configuration of an incomplete star graph and its routing algorithm;and improving hierarchical power saving technique with location information for sensor networks.
parallel and distributedprocessingtechniques and applications is a compendium of articles and papers that were presented at PDPTA '13, an internationalconference that serves researchers, scholars, professionals...
ISBN:
(纸本)9781601322586
parallel and distributedprocessingtechniques and applications is a compendium of articles and papers that were presented at PDPTA '13, an internationalconference that serves researchers, scholars, professionals, students, and academicians. Hamid R. Arabnia, Hiroshi Ishii, Minoru Ito, Hiroaki Nishikawa, Fernando G. Tinetti, George A. Gravvanis, George Jandieri, and Ashu M. G. Solo (Editors) Selected topics include parallel and distributed algorithms and applications, mathematical modeling and problem solving, and grid + cloud computing and supporting tools + applications, etc. Selected Sessions and Topics for example: Resource Allocation, Scheduling, Energy-Aware Computing + Load-Balancing + Fault-Tolerant Systems parallel and distributed Algorithms and applications Mathematical Modeling and Problem Solving - MPS Grid + Cloud Computing and Supporting Tools + applications Systems Software + Programming Models + Threads + Caching + File Systems + Testing and Monitoring Methods Performance Evaluation, Estimation, and Related Issues Communication Systems + Networks and Interconnection Networks + Peer-To-Peer Networks Ad-Hoc Networks + Sensor Networks and applications Cluster Computing + Multi-Core, GPU, FPGA processing and applications Data-Driven Networking Systems with High Tolerance for Disaster, Fault and Congestion Position and Regular - Communication Systems, Cloud Computing, Reconfigurable Systems, parallel and distributed Computing, Scheduling, Architectures, and applications Cloud Computing, Optimization, Sensor Networks, Scheduling, and applications
This special issue is dedicated to examining the rapidly evolving fields of artificial intelligence, mathematical modeling, and optimization, with particular emphasis on their growing importance in computational scien...
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This special issue is dedicated to examining the rapidly evolving fields of artificial intelligence, mathematical modeling, and optimization, with particular emphasis on their growing importance in computational science. It features the most notable papers from the "Mathematical Modeling and Problem Solving" workshop at PDPTA'24, the 30th international conference on parallel and distributed processing techniques and applications. The issue showcases pioneering research in areas such as natural language processing, system optimization, and high-performance computing. The nine selected studies include novel AI-driven methods for chemical compound generation, historical text recognition, and music recommendation, along with advancements in hardware optimization through reconfigurable accelerators and vector register sharing. Additionally, evolutionary and hyper-heuristic algorithms are explored for sophisticated problem-solving in engineering design, and innovative techniques are introduced for high-speed numerical methods in large-scale systems. Collectively, these contributions demonstrate the significance of AI, supercomputing, and advanced algorithms in driving the next generation of scientific discovery.
The proceedings contain 24 papers. The special focus in this conference is on parallel and distributedprocessingtechniques. The topics include: parallel N-Body Performance Comparison: Julia, Rust, and More;REFT...
ISBN:
(纸本)9783031856372
The proceedings contain 24 papers. The special focus in this conference is on parallel and distributedprocessingtechniques. The topics include: parallel N-Body Performance Comparison: Julia, Rust, and More;REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments;An Efficient Data Provenance Collection Framework for HPC I/O Workloads;using Minicasts for Efficient Asynchronous Causal Unicast and Byzantine Tolerance;a Comparative Study of Two Matrix Multiplication Algorithms Under Current Hardware Architectures;Is Manual Code Optimization Still Required to Mitigate GPU Thread Divergence? Applying a Flattening Technique to Observe Performance;towards Automatic, Predictable and High-Performance parallel Code Generation;Attack Graph Generation on HPC Clusters;analyzing the Influence of File Formats on I/O Patterns in Deep Learning;inference of Cell–Cell Interactions Through Spatial Transcriptomics Data Using Graph Convolutional Neural Networks;natural Product-Like Compound Generation with Chemical Language Models;improved Early–Modern Japanese Printed Character Recognition Rate with Generated Characters;Improved Method for Similar Music Recommendation Using Spotify API;Reconfigurable Virtual Accelerator (ReVA) for Large-Scale Acceleration Circuits;Building Simulation Environment of Reconfigurable Virtual Accelerator (ReVA);vector Register Sharing Mechanism for High Performance Hardware Acceleration;Efficient Compute Resource Sharing of RISC-V Packed-SIMD Using Simultaneous Multi-threading;introducing Competitive Mechanism to Differential Evolution for Numerical Optimization;hyper-heuristic Differential Evolution with Novel Boundary Repair for Numerical Optimization;jump Like a Frog: Optimization of Renewable Energy Prediction in Smart Gird Based on Ultra Long Term Network;vision Transformer-Based Meta Loss Landscape Exploration with Actor-Critic Method;Fast Computation Method for Stopping Condition of Range Restricted
Real-time optical image processing has become a critical technology in domains such as autonomous systems, medical diagnostics, and surveillance. However, traditional centralized processing approaches face challenges ...
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Linear algebra algorithms, such as the Householder QR decomposition, are pivotal in various applications including signal processing, optimization, and numerical solutions to systems of linear equations. Traditional s...
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ISBN:
(纸本)9783031814037;9783031814044
Linear algebra algorithms, such as the Householder QR decomposition, are pivotal in various applications including signal processing, optimization, and numerical solutions to systems of linear equations. Traditional sequential implementations of the Householder algorithm face significant limitations in terms of performance and scalability when applied to large matrices. To overcome these constraints, this paper explores the parallelization of the Householder QR algorithm on Graphics processing Units (GPUs) using CUDA, a parallel computing platform and programming model developed by NVIDIA. Our method ensures the availability of critical intermediate data, distinguishing it from standard libraries like cuSOLVER, which modify the processing order and often discard important intermediate computations. By leveraging CUDA streams, we achieve enhanced parallelism without compromising the integrity of the algorithm's sequence or the accessibility of intermediate data. Our performance analysis reveals that our implementation achieves efficiency comparable to cuSOLVER, making it a viable option. This study not only presents a novel implementation but also extends the potential for GPU-accelerated linear algebra procedures to benefit a wider range of scientific and engineering applications.
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